# COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

arXiv: https://arxiv.org/abs/2004.05835 · HTML: https://mlsanity.com/paper/2004.05835/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images](https://mlsanity.com/paper/2105.08199/) (2021)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays](https://mlsanity.com/paper/2104.04006/) (2021)
- [HiClass: a Python library for local hierarchical classification compatible with scikit-learn](https://mlsanity.com/paper/2112.06560/) (2021)
- [Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation](https://mlsanity.com/paper/2112.02478/) (2021)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images](https://mlsanity.com/paper/2410.15437/) (2024)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification](https://mlsanity.com/paper/2110.11148/) (2021)
- [Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP](https://mlsanity.com/paper/2103.09904/) (2021)
- [Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review](https://mlsanity.com/paper/2206.05615/) (2022)
- [A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT](https://mlsanity.com/paper/2207.07117/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph](https://mlsanity.com/paper/2402.18610/) (2024)
- [Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction](https://mlsanity.com/paper/2405.18507/) (2024)
- [A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics](https://mlsanity.com/paper/2412.07818/) (2024)
- [Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images](https://mlsanity.com/paper/2412.19362/) (2024)

## Abstract (arXiv)

The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. CXR are useful in because it is cheaper, faster and more widespread than CT. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. In order to achieve the objectives, we have proposed a classification schema considering the multi-class and hierarchical perspectives, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in order to re-balance the classes distribution. Our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in order to leverage the strength of multiple texture descriptors and base classifiers at once. To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others. The proposed approach achieved a macro-avg F1-Score of 0.65 using a multi-class approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario. As far as we know, we achieved the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here.